The approaches described in this section are approaches that could be pursued, but not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section.
Data analysts in the financial services industry often perform analysis of a large collection of data items, such as data relating to market instruments. In many instances, the amount of raw data about data items can be massive and dynamically increasing all the time. For example, instruments may be daily traded in large volumes and numerous times. Therefore, in addition to metadata that captures relatively stable aspects of the instruments, a huge amount of raw trading data may be accumulated over a particular period of time, such as the past six months.
While an instrument can possibly be analyzed based on raw trading data, it is often difficult to make sense of the raw trading data, metadata, or related computations. This problem is drastically compounded when analyzing a large collection of instruments, because numerous factors influence trading of the instruments in numerous different ways. Thus, an analyst often is forced to rely on inexact hunches, experience, and cumbersome spreadsheets to make forecasts relating to instruments.